Answering SPARQL queries on the web of data through zero-knowledge link traversal

Abstract

Link traversal has emerged as a SPARQL query processing method that exploits the Linked Data principles to dynamically discover data relevant for answering a query by dereferencing online Web resources (URIs) at query execution time. While several approaches for such a lookup-based query evaluation method have been proposed, there exists no analysis of the types (patterns) of queries that can be directly answered on the Web of Data through a "zero-knowledge" approach, i.e., without accessing local or remote endpoints and without a-priori knowledge of available data sources. In this paper, we first provide a method for examining if a SPARQL query can be answered through zero-knowledge link traversal and analyse a large corpus of real SPARQL query logs for finding the frequency and distribution of answerable and non-answerable query patterns. Subsequently, we provide an algorithm for transforming answerable queries to SPARQL-LD queries that bypass the endpoints, as well as a method to estimate their evaluation cost which can be useful for deciding on the query execution strategy to follow. We report experimental results about the efficiency of the transformed queries and discuss the benefits and the limitations of this query evaluation method.